Overview

Dataset statistics

Number of variables21
Number of observations16
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory176.0 B

Variable types

DateTime1
Numeric20

Alerts

dispute is highly correlated with total and 4 other fieldsHigh correlation
non_violent_crises is highly correlated with violent_crises and 8 other fieldsHigh correlation
violent_crises is highly correlated with non_violent_crises and 9 other fieldsHigh correlation
limited_wars is highly correlated with non_violent_crises and 4 other fieldsHigh correlation
wars is highly correlated with non_violent_crises and 11 other fieldsHigh correlation
total is highly correlated with dispute and 8 other fieldsHigh correlation
gni_ger is highly correlated with wars and 10 other fieldsHigh correlation
gni_fra is highly correlated with dispute and 7 other fieldsHigh correlation
gni_ita is highly correlated with dispute and 1 other fieldsHigh correlation
gni_jpn is highly correlated with dispute and 5 other fieldsHigh correlation
gni_can is highly correlated with non_violent_crises and 14 other fieldsHigh correlation
gni_rus is highly correlated with wars and 9 other fieldsHigh correlation
gni_usa is highly correlated with non_violent_crises and 9 other fieldsHigh correlation
gni_gbr is highly correlated with gni_mexHigh correlation
gni_bra is highly correlated with dispute and 9 other fieldsHigh correlation
gni_ind is highly correlated with non_violent_crises and 9 other fieldsHigh correlation
gni_mex is highly correlated with violent_crises and 13 other fieldsHigh correlation
gni_zaf is highly correlated with total and 7 other fieldsHigh correlation
gni_chn is highly correlated with non_violent_crises and 9 other fieldsHigh correlation
gni_wld is highly correlated with non_violent_crises and 10 other fieldsHigh correlation
dispute is highly correlated with total and 6 other fieldsHigh correlation
non_violent_crises is highly correlated with violent_crises and 7 other fieldsHigh correlation
violent_crises is highly correlated with non_violent_crises and 12 other fieldsHigh correlation
limited_wars is highly correlated with non_violent_crises and 2 other fieldsHigh correlation
wars is highly correlated with non_violent_crises and 12 other fieldsHigh correlation
total is highly correlated with dispute and 10 other fieldsHigh correlation
gni_ger is highly correlated with violent_crises and 12 other fieldsHigh correlation
gni_fra is highly correlated with dispute and 8 other fieldsHigh correlation
gni_ita is highly correlated with dispute and 2 other fieldsHigh correlation
gni_jpn is highly correlated with dispute and 4 other fieldsHigh correlation
gni_can is highly correlated with violent_crises and 13 other fieldsHigh correlation
gni_rus is highly correlated with dispute and 11 other fieldsHigh correlation
gni_usa is highly correlated with non_violent_crises and 8 other fieldsHigh correlation
gni_gbr is highly correlated with gni_mexHigh correlation
gni_bra is highly correlated with dispute and 11 other fieldsHigh correlation
gni_ind is highly correlated with non_violent_crises and 9 other fieldsHigh correlation
gni_mex is highly correlated with violent_crises and 13 other fieldsHigh correlation
gni_zaf is highly correlated with dispute and 11 other fieldsHigh correlation
gni_chn is highly correlated with non_violent_crises and 9 other fieldsHigh correlation
gni_wld is highly correlated with non_violent_crises and 12 other fieldsHigh correlation
non_violent_crises is highly correlated with violent_crises and 3 other fieldsHigh correlation
violent_crises is highly correlated with non_violent_crises and 6 other fieldsHigh correlation
wars is highly correlated with non_violent_crises and 5 other fieldsHigh correlation
total is highly correlated with violent_crises and 5 other fieldsHigh correlation
gni_ger is highly correlated with gni_fra and 7 other fieldsHigh correlation
gni_fra is highly correlated with gni_ger and 2 other fieldsHigh correlation
gni_ita is highly correlated with gni_fraHigh correlation
gni_can is highly correlated with wars and 7 other fieldsHigh correlation
gni_rus is highly correlated with total and 6 other fieldsHigh correlation
gni_usa is highly correlated with violent_crises and 5 other fieldsHigh correlation
gni_bra is highly correlated with wars and 5 other fieldsHigh correlation
gni_ind is highly correlated with non_violent_crises and 5 other fieldsHigh correlation
gni_mex is highly correlated with total and 6 other fieldsHigh correlation
gni_zaf is highly correlated with gni_can and 2 other fieldsHigh correlation
gni_chn is highly correlated with non_violent_crises and 5 other fieldsHigh correlation
gni_wld is highly correlated with violent_crises and 7 other fieldsHigh correlation
year is highly correlated with dispute and 19 other fieldsHigh correlation
dispute is highly correlated with year and 15 other fieldsHigh correlation
non_violent_crises is highly correlated with year and 15 other fieldsHigh correlation
violent_crises is highly correlated with year and 13 other fieldsHigh correlation
limited_wars is highly correlated with year and 6 other fieldsHigh correlation
wars is highly correlated with year and 10 other fieldsHigh correlation
total is highly correlated with year and 10 other fieldsHigh correlation
gni_ger is highly correlated with year and 11 other fieldsHigh correlation
gni_fra is highly correlated with year and 15 other fieldsHigh correlation
gni_ita is highly correlated with year and 9 other fieldsHigh correlation
gni_jpn is highly correlated with year and 14 other fieldsHigh correlation
gni_can is highly correlated with year and 17 other fieldsHigh correlation
gni_rus is highly correlated with year and 13 other fieldsHigh correlation
gni_usa is highly correlated with year and 15 other fieldsHigh correlation
gni_gbr is highly correlated with year and 12 other fieldsHigh correlation
gni_bra is highly correlated with year and 15 other fieldsHigh correlation
gni_ind is highly correlated with year and 14 other fieldsHigh correlation
gni_mex is highly correlated with year and 12 other fieldsHigh correlation
gni_zaf is highly correlated with year and 11 other fieldsHigh correlation
gni_chn is highly correlated with year and 16 other fieldsHigh correlation
gni_wld is highly correlated with year and 15 other fieldsHigh correlation
year has unique values Unique
violent_crises has unique values Unique
total has unique values Unique
gni_ger has unique values Unique
gni_fra has unique values Unique
gni_ita has unique values Unique
gni_jpn has unique values Unique
gni_can has unique values Unique
gni_rus has unique values Unique
gni_usa has unique values Unique
gni_gbr has unique values Unique
gni_bra has unique values Unique
gni_ind has unique values Unique
gni_mex has unique values Unique
gni_zaf has unique values Unique
gni_chn has unique values Unique
gni_wld has unique values Unique

Reproduction

Analysis started2022-07-04 11:40:18.790129
Analysis finished2022-07-04 11:41:01.080482
Duration42.29 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

year
Date

HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size256.0 B
Minimum1970-01-01 00:00:00.000002
Maximum1970-01-01 00:00:00.000002
2022-07-04T13:41:01.167502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:01.273525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)

dispute
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.375
Minimum63
Maximum107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:41:01.378549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum63
5-th percentile66.75
Q170.5
median80.5
Q397.5
95-th percentile107
Maximum107
Range44
Interquartile range (IQR)27

Descriptive statistics

Standard deviation15.61996586
Coefficient of variation (CV)0.1851255213
Kurtosis-1.540707544
Mean84.375
Median Absolute Deviation (MAD)12.5
Skewness0.2549791868
Sum1350
Variance243.9833333
MonotonicityNot monotonic
2022-07-04T13:41:01.480572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1072
12.5%
682
12.5%
631
 
6.2%
721
 
6.2%
791
 
6.2%
821
 
6.2%
951
 
6.2%
1061
 
6.2%
991
 
6.2%
971
 
6.2%
Other values (4)4
25.0%
ValueCountFrequency (%)
631
6.2%
682
12.5%
691
6.2%
711
6.2%
721
6.2%
771
6.2%
791
6.2%
821
6.2%
901
6.2%
951
6.2%
ValueCountFrequency (%)
1072
12.5%
1061
6.2%
991
6.2%
971
6.2%
951
6.2%
901
6.2%
821
6.2%
791
6.2%
771
6.2%
721
6.2%

non_violent_crises
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.5
Minimum70
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:41:01.580595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile71.5
Q182.75
median88
Q3109.5
95-th percentile127
Maximum130
Range60
Interquartile range (IQR)26.75

Descriptive statistics

Standard deviation18.82197297
Coefficient of variation (CV)0.1991743172
Kurtosis-0.6739379008
Mean94.5
Median Absolute Deviation (MAD)8.5
Skewness0.7089213253
Sum1512
Variance354.2666667
MonotonicityNot monotonic
2022-07-04T13:41:01.745632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
882
 
12.5%
931
 
6.2%
1141
 
6.2%
1261
 
6.2%
1301
 
6.2%
1181
 
6.2%
1081
 
6.2%
871
 
6.2%
851
 
6.2%
821
 
6.2%
Other values (5)5
31.2%
ValueCountFrequency (%)
701
6.2%
721
6.2%
771
6.2%
821
6.2%
831
6.2%
851
6.2%
871
6.2%
882
12.5%
911
6.2%
931
6.2%
ValueCountFrequency (%)
1301
6.2%
1261
6.2%
1181
6.2%
1141
6.2%
1081
6.2%
931
6.2%
911
6.2%
882
12.5%
871
6.2%
851
6.2%

violent_crises
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.9375
Minimum90
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:41:01.847656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile99
Q1109.25
median165.5
Q3180.25
95-th percentile188.5
Maximum190
Range100
Interquartile range (IQR)71

Descriptive statistics

Standard deviation36.24080343
Coefficient of variation (CV)0.2401047018
Kurtosis-1.457733166
Mean150.9375
Median Absolute Deviation (MAD)20
Skewness-0.5732856418
Sum2415
Variance1313.395833
MonotonicityNot monotonic
2022-07-04T13:41:01.949678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
901
 
6.2%
1041
 
6.2%
1071
 
6.2%
1021
 
6.2%
1101
 
6.2%
1391
 
6.2%
1551
 
6.2%
1771
 
6.2%
1781
 
6.2%
1811
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
901
6.2%
1021
6.2%
1041
6.2%
1071
6.2%
1101
6.2%
1391
6.2%
1551
6.2%
1581
6.2%
1731
6.2%
1771
6.2%
ValueCountFrequency (%)
1901
6.2%
1881
6.2%
1831
6.2%
1811
6.2%
1801
6.2%
1781
6.2%
1771
6.2%
1731
6.2%
1581
6.2%
1551
6.2%

limited_wars
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.1875
Minimum16
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:41:02.045700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile18.25
Q121.75
median25
Q326
95-th percentile30.25
Maximum31
Range15
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation4.16683333
Coefficient of variation (CV)0.172272179
Kurtosis-0.2284627711
Mean24.1875
Median Absolute Deviation (MAD)2.5
Skewness-0.1575500079
Sum387
Variance17.3625
MonotonicityNot monotonic
2022-07-04T13:41:02.144722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
254
25.0%
262
12.5%
302
12.5%
192
12.5%
221
 
6.2%
311
 
6.2%
241
 
6.2%
211
 
6.2%
161
 
6.2%
231
 
6.2%
ValueCountFrequency (%)
161
 
6.2%
192
12.5%
211
 
6.2%
221
 
6.2%
231
 
6.2%
241
 
6.2%
254
25.0%
262
12.5%
302
12.5%
311
 
6.2%
ValueCountFrequency (%)
311
 
6.2%
302
12.5%
262
12.5%
254
25.0%
241
 
6.2%
231
 
6.2%
221
 
6.2%
211
 
6.2%
192
12.5%
161
 
6.2%

wars
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14
Minimum2
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:41:02.241744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q17.5
median17
Q319.25
95-th percentile20.25
Maximum21
Range19
Interquartile range (IQR)11.75

Descriptive statistics

Standard deviation6.582805886
Coefficient of variation (CV)0.4702004204
Kurtosis-1.406969764
Mean14
Median Absolute Deviation (MAD)3
Skewness-0.5785301457
Sum224
Variance43.33333333
MonotonicityNot monotonic
2022-07-04T13:41:02.342767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
63
18.8%
203
18.8%
193
18.8%
21
 
6.2%
91
 
6.2%
81
 
6.2%
181
 
6.2%
161
 
6.2%
151
 
6.2%
211
 
6.2%
ValueCountFrequency (%)
21
 
6.2%
63
18.8%
81
 
6.2%
91
 
6.2%
151
 
6.2%
161
 
6.2%
181
 
6.2%
193
18.8%
203
18.8%
211
 
6.2%
ValueCountFrequency (%)
211
 
6.2%
203
18.8%
193
18.8%
181
 
6.2%
161
 
6.2%
151
 
6.2%
91
 
6.2%
81
 
6.2%
63
18.8%
21
 
6.2%

total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean368
Minimum274
Maximum418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:41:02.442790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum274
5-th percentile313
Q1356.75
median369
Q3391.25
95-th percentile412
Maximum418
Range144
Interquartile range (IQR)34.5

Descriptive statistics

Standard deviation35.55090247
Coefficient of variation (CV)0.09660571324
Kurtosis2.194012998
Mean368
Median Absolute Deviation (MAD)17
Skewness-1.051035019
Sum5888
Variance1263.866667
MonotonicityNot monotonic
2022-07-04T13:41:02.546813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2741
 
6.2%
3261
 
6.2%
3441
 
6.2%
3531
 
6.2%
3681
 
6.2%
3701
 
6.2%
3871
 
6.2%
4051
 
6.2%
4181
 
6.2%
4101
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
2741
6.2%
3261
6.2%
3441
6.2%
3531
6.2%
3581
6.2%
3591
6.2%
3651
6.2%
3681
6.2%
3701
6.2%
3711
6.2%
ValueCountFrequency (%)
4181
6.2%
4101
6.2%
4051
6.2%
4041
6.2%
3871
6.2%
3761
6.2%
3711
6.2%
3701
6.2%
3681
6.2%
3651
6.2%

gni_ger
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.638384766 × 1012
Minimum2.870368809 × 1012
Maximum4.105202181 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:41:02.646836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.870368809 × 1012
5-th percentile3.001607134 × 1012
Q13.473455584 × 1012
median3.695280701 × 1012
Q33.872360393 × 1012
95-th percentile4.037096174 × 1012
Maximum4.105202181 × 1012
Range1.234833373 × 1012
Interquartile range (IQR)3.989048094 × 1011

Descriptive statistics

Standard deviation3.41709804 × 1011
Coefficient of variation (CV)0.09391799548
Kurtosis0.4432537896
Mean3.638384766 × 1012
Median Absolute Deviation (MAD)2.236894168 × 1011
Skewness-0.8424232922
Sum5.821415626 × 1013
Variance1.167655902 × 1023
MonotonicityNot monotonic
2022-07-04T13:41:02.751860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2.870368809 × 10121
 
6.2%
3.045353243 × 10121
 
6.2%
3.475319883 × 10121
 
6.2%
3.780819607 × 10121
 
6.2%
3.488175095 × 10121
 
6.2%
3.467862685 × 10121
 
6.2%
3.845325105 × 10121
 
6.2%
3.611772219 × 10121
 
6.2%
3.820263806 × 10121
 
6.2%
3.967012279 × 10121
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
2.870368809 × 10121
6.2%
3.045353243 × 10121
6.2%
3.434101057 × 10121
6.2%
3.467862685 × 10121
6.2%
3.475319883 × 10121
6.2%
3.488175095 × 10121
6.2%
3.555930678 × 10121
6.2%
3.611772219 × 10121
6.2%
3.778789184 × 10121
6.2%
3.780819607 × 10121
6.2%
ValueCountFrequency (%)
4.105202181 × 10121
6.2%
4.014394172 × 10121
6.2%
3.967012279 × 10121
6.2%
3.953466259 × 10121
6.2%
3.845325105 × 10121
6.2%
3.820263806 × 10121
6.2%
3.780819607 × 10121
6.2%
3.778789184 × 10121
6.2%
3.611772219 × 10121
6.2%
3.555930678 × 10121
6.2%

gni_fra
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.702548066 × 1012
Minimum2.234564034 × 1012
Maximum2.994136039 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:41:02.852883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.234564034 × 1012
5-th percentile2.333864383 × 1012
Q12.621673063 × 1012
median2.727895146 × 1012
Q32.860461094 × 1012
95-th percentile2.954530098 × 1012
Maximum2.994136039 × 1012
Range7.595720048 × 1011
Interquartile range (IQR)2.387880309 × 1011

Descriptive statistics

Standard deviation2.105341784 × 1011
Coefficient of variation (CV)0.07790210323
Kurtosis0.2335983206
Mean2.702548066 × 1012
Median Absolute Deviation (MAD)1.373656651 × 1011
Skewness-0.7949130134
Sum4.324076905 × 1013
Variance4.432464027 × 1022
MonotonicityNot monotonic
2022-07-04T13:41:02.959907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2.234564034 × 10121
 
6.2%
2.366964499 × 10121
 
6.2%
2.713440991 × 10121
 
6.2%
2.994136039 × 10121
 
6.2%
2.763004002 × 10121
 
6.2%
2.706418758 × 10121
 
6.2%
2.941328117 × 10121
 
6.2%
2.7423493 × 10121
 
6.2%
2.874860245 × 10121
 
6.2%
2.917864654 × 10121
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
2.234564034 × 10121
6.2%
2.366964499 × 10121
6.2%
2.491864987 × 10121
6.2%
2.525276586 × 10121
6.2%
2.653805222 × 10121
6.2%
2.671813716 × 10121
6.2%
2.706418758 × 10121
6.2%
2.713440991 × 10121
6.2%
2.7423493 × 10121
6.2%
2.763004002 × 10121
6.2%
ValueCountFrequency (%)
2.994136039 × 10121
6.2%
2.941328117 × 10121
6.2%
2.917864654 × 10121
6.2%
2.874860245 × 10121
6.2%
2.855661376 × 10121
6.2%
2.787416525 × 10121
6.2%
2.763004002 × 10121
6.2%
2.7423493 × 10121
6.2%
2.713440991 × 10121
6.2%
2.706418758 × 10121
6.2%

gni_ita
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.072653288 × 1012
Minimum1.823940556 × 1012
Maximum2.386641522 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:41:03.133946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.823940556 × 1012
5-th percentile1.85183735 × 1012
Q11.946955265 × 1012
median2.0997216 × 1012
Q32.171284778 × 1012
95-th percentile2.313552659 × 1012
Maximum2.386641522 × 1012
Range5.627009656 × 1011
Interquartile range (IQR)2.243295127 × 1011

Descriptive statistics

Standard deviation1.616245769 × 1011
Coefficient of variation (CV)0.07797955296
Kurtosis-0.6870341634
Mean2.072653288 × 1012
Median Absolute Deviation (MAD)1.210378047 × 1011
Skewness0.1635604257
Sum3.316245261 × 1013
Variance2.612250385 × 1022
MonotonicityNot monotonic
2022-07-04T13:41:03.236969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1.861136281 × 10121
 
6.2%
1.957286083 × 10121
 
6.2%
2.21481051 × 10121
 
6.2%
2.386641522 × 10121
 
6.2%
2.198543925 × 10121
 
6.2%
2.131574091 × 10121
 
6.2%
2.289189704 × 10121
 
6.2%
2.08440658 × 10121
 
6.2%
2.138212004 × 10121
 
6.2%
2.162198396 × 10121
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
1.823940556 × 10121
6.2%
1.861136281 × 10121
6.2%
1.882461088 × 10121
6.2%
1.915962814 × 10121
6.2%
1.957286083 × 10121
6.2%
1.9727349 × 10121
6.2%
2.02831754 × 10121
6.2%
2.08440658 × 10121
6.2%
2.11503662 × 10121
6.2%
2.131574091 × 10121
6.2%
ValueCountFrequency (%)
2.386641522 × 10121
6.2%
2.289189704 × 10121
6.2%
2.21481051 × 10121
6.2%
2.198543925 × 10121
6.2%
2.162198396 × 10121
6.2%
2.138212004 × 10121
6.2%
2.131574091 × 10121
6.2%
2.11503662 × 10121
6.2%
2.08440658 × 10121
6.2%
2.02831754 × 10121
6.2%

gni_jpn
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.31127761 × 1012
Minimum4.619771169 × 1012
Maximum6.445536591 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:41:03.338992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4.619771169 × 1012
5-th percentile4.695012089 × 1012
Q15.043916653 × 1012
median5.226759535 × 1012
Q35.399313274 × 1012
95-th percentile6.422181817 × 1012
Maximum6.445536591 × 1012
Range1.825765422 × 1012
Interquartile range (IQR)3.55396621 × 1011

Descriptive statistics

Standard deviation5.369279547 × 1011
Coefficient of variation (CV)0.1010920524
Kurtosis0.7989322428
Mean5.31127761 × 1012
Median Absolute Deviation (MAD)1.802613902 × 1011
Skewness1.085664684
Sum8.498044176 × 1013
Variance2.882916285 × 1023
MonotonicityNot monotonic
2022-07-04T13:41:03.443016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
4.938602699 × 10121
 
6.2%
4.724628609 × 10121
 
6.2%
4.720092396 × 10121
 
6.2%
5.243862441 × 10121
 
6.2%
5.422436227 × 10121
 
6.2%
5.912477091 × 10121
 
6.2%
6.414396892 × 10121
 
6.2%
6.445536591 × 10121
 
6.2%
5.391605624 × 10121
 
6.2%
5.079021305 × 10121
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
4.619771169 × 10121
6.2%
4.720092396 × 10121
6.2%
4.724628609 × 10121
6.2%
4.938602699 × 10121
6.2%
5.079021305 × 10121
6.2%
5.113251597 × 10121
6.2%
5.177794688 × 10121
6.2%
5.22288721 × 10121
6.2%
5.230631861 × 10121
6.2%
5.243862441 × 10121
6.2%
ValueCountFrequency (%)
6.445536591 × 10121
6.2%
6.414396892 × 10121
6.2%
5.912477091 × 10121
6.2%
5.422436227 × 10121
6.2%
5.391605624 × 10121
6.2%
5.323445359 × 10121
6.2%
5.243862441 × 10121
6.2%
5.230631861 × 10121
6.2%
5.22288721 × 10121
6.2%
5.177794688 × 10121
6.2%

gni_can
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.576532923 × 1012
Minimum1.149135996 × 1012
Maximum1.818471076 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:41:03.547039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.149135996 × 1012
5-th percentile1.261006672 × 1012
Q11.493796064 × 1012
median1.606214395 × 1012
Q31.729592348 × 1012
95-th percentile1.801557671 × 1012
Maximum1.818471076 × 1012
Range6.693350797 × 1011
Interquartile range (IQR)2.357962835 × 1011

Descriptive statistics

Standard deviation1.926111343 × 1011
Coefficient of variation (CV)0.1221738738
Kurtosis0.009101393585
Mean1.576532923 × 1012
Median Absolute Deviation (MAD)1.33331761 × 1011
Skewness-0.7462736114
Sum2.522452677 × 1013
Variance3.709904907 × 1022
MonotonicityNot monotonic
2022-07-04T13:41:03.650063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1.149135996 × 10121
 
6.2%
1.298296897 × 10121
 
6.2%
1.447200447 × 10121
 
6.2%
1.530173383 × 10121
 
6.2%
1.350956172 × 10121
 
6.2%
1.585379876 × 10121
 
6.2%
1.759453773 × 10121
 
6.2%
1.79591987 × 10121
 
6.2%
1.818471076 × 10121
 
6.2%
1.776572161 × 10121
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
1.149135996 × 10121
6.2%
1.298296897 × 10121
6.2%
1.350956172 × 10121
6.2%
1.447200447 × 10121
6.2%
1.509327937 × 10121
6.2%
1.530173383 × 10121
6.2%
1.532663548 × 10121
6.2%
1.585379876 × 10121
6.2%
1.627048915 × 10121
6.2%
1.628291572 × 10121
6.2%
ValueCountFrequency (%)
1.818471076 × 10121
6.2%
1.79591987 × 10121
6.2%
1.776572161 × 10121
6.2%
1.759453773 × 10121
6.2%
1.719638539 × 10121
6.2%
1.695996609 × 10121
6.2%
1.628291572 × 10121
6.2%
1.627048915 × 10121
6.2%
1.585379876 × 10121
6.2%
1.532663548 × 10121
6.2%

gni_rus
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.524493966 × 1012
Minimum7.45490708 × 1011
Maximum2.212868847 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:41:03.752086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum7.45490708 × 1011
5-th percentile9.072180337 × 1011
Q11.263480543 × 1012
median1.504979428 × 1012
Q31.726076568 × 1012
95-th percentile2.158693442 × 1012
Maximum2.212868847 × 1012
Range1.467378139 × 1012
Interquartile range (IQR)4.625960243 × 1011

Descriptive statistics

Standard deviation4.119908457 × 1011
Coefficient of variation (CV)0.2702476067
Kurtosis-0.4013277413
Mean1.524493966 × 1012
Median Absolute Deviation (MAD)2.488956057 × 1011
Skewness0.02626087413
Sum2.439190345 × 1013
Variance1.69736457 × 1023
MonotonicityNot monotonic
2022-07-04T13:41:03.855748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7.45490708 × 10111
 
6.2%
9.611271423 × 10111
 
6.2%
1.270877265 × 10121
 
6.2%
1.614363888 × 10121
 
6.2%
1.182904882 × 10121
 
6.2%
1.477812768 × 10121
 
6.2%
1.985526208 × 10121
 
6.2%
2.140634974 × 10121
 
6.2%
2.212868847 × 10121
 
6.2%
1.991279765 × 10121
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
7.45490708 × 10111
6.2%
9.611271423 × 10111
6.2%
1.182904882 × 10121
6.2%
1.241290379 × 10121
6.2%
1.270877265 × 10121
6.2%
1.325732263 × 10121
6.2%
1.453317165 × 10121
6.2%
1.477812768 × 10121
6.2%
1.532146087 × 10121
6.2%
1.614363888 × 10121
6.2%
ValueCountFrequency (%)
2.212868847 × 10121
6.2%
2.140634974 × 10121
6.2%
1.991279765 × 10121
6.2%
1.985526208 × 10121
6.2%
1.639593354 × 10121
6.2%
1.616937756 × 10121
6.2%
1.614363888 × 10121
6.2%
1.532146087 × 10121
6.2%
1.477812768 × 10121
6.2%
1.453317165 × 10121
6.2%

gni_usa
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.721081119 × 1013
Minimum1.3166516 × 1013
Maximum2.170865 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:41:03.960772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.3166516 × 1013
5-th percentile1.38414155 × 1013
Q11.4676109 × 1013
median1.6931963 × 1013
Q31.92386275 × 1013
95-th percentile2.139214025 × 1013
Maximum2.170865 × 1013
Range8.542134 × 1012
Interquartile range (IQR)4.5625185 × 1012

Descriptive statistics

Standard deviation2.800605927 × 1012
Coefficient of variation (CV)0.1627236448
Kurtosis-1.308001909
Mean1.721081119 × 1013
Median Absolute Deviation (MAD)2.297856 × 1012
Skewness0.2615082741
Sum2.75372979 × 1014
Variance7.843393558 × 1024
MonotonicityNot monotonic
2022-07-04T13:41:04.062796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1.3166516 × 10131
 
6.2%
1.4066382 × 10131
 
6.2%
1.4550103 × 10131
 
6.2%
1.4718111 × 10131
 
6.2%
1.4426269 × 10131
 
6.2%
1.5172073 × 10131
 
6.2%
1.5849978 × 10131
 
6.2%
1.6675595 × 10131
 
6.2%
1.7188331 × 10131
 
6.2%
1.8043094 × 10131
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
1.3166516 × 10131
6.2%
1.4066382 × 10131
6.2%
1.4426269 × 10131
6.2%
1.4550103 × 10131
6.2%
1.4718111 × 10131
6.2%
1.5172073 × 10131
6.2%
1.5849978 × 10131
6.2%
1.6675595 × 10131
6.2%
1.7188331 × 10131
6.2%
1.8043094 × 10131
6.2%
ValueCountFrequency (%)
2.170865 × 10131
6.2%
2.1286637 × 10131
6.2%
2.0946778 × 10131
6.2%
1.9893073 × 10131
6.2%
1.9020479 × 10131
6.2%
1.866091 × 10131
6.2%
1.8043094 × 10131
6.2%
1.7188331 × 10131
6.2%
1.6675595 × 10131
6.2%
1.5849978 × 10131
6.2%

gni_gbr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.749608735 × 1012
Minimum2.406924755 × 1012
Maximum3.09115046 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:41:04.163819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.406924755 × 1012
5-th percentile2.470834411 × 1012
Q12.661664661 × 1012
median2.721217452 × 1012
Q32.868022351 × 1012
95-th percentile3.040214509 × 1012
Maximum3.09115046 × 1012
Range6.842257055 × 1011
Interquartile range (IQR)2.0635769 × 1011

Descriptive statistics

Standard deviation1.824995103 × 1011
Coefficient of variation (CV)0.06637290169
Kurtosis-0.1386475966
Mean2.749608735 × 1012
Median Absolute Deviation (MAD)1.398168278 × 1011
Skewness0.06892190415
Sum4.399373977 × 1013
Variance3.330607125 × 1022
MonotonicityNot monotonic
2022-07-04T13:41:04.267842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2.578605455 × 10121
 
6.2%
2.71926035 × 10121
 
6.2%
3.09115046 × 10121
 
6.2%
2.910742647 × 10121
 
6.2%
2.406924755 × 10121
 
6.2%
2.492137629 × 10121
 
6.2%
2.684173033 × 10121
 
6.2%
2.69110987 × 10121
 
6.2%
2.746456576 × 10121
 
6.2%
3.023235858 × 10121
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
2.406924755 × 10121
6.2%
2.492137629 × 10121
6.2%
2.578605455 × 10121
6.2%
2.654615868 × 10121
6.2%
2.664014258 × 10121
6.2%
2.684173033 × 10121
6.2%
2.69110987 × 10121
6.2%
2.71926035 × 10121
6.2%
2.723174555 × 10121
6.2%
2.746456576 × 10121
6.2%
ValueCountFrequency (%)
3.09115046 × 10121
6.2%
3.023235858 × 10121
6.2%
2.910742647 × 10121
6.2%
2.886069892 × 10121
6.2%
2.862006504 × 10121
6.2%
2.860062056 × 10121
6.2%
2.746456576 × 10121
6.2%
2.723174555 × 10121
6.2%
2.71926035 × 10121
6.2%
2.69110987 × 10121
6.2%

gni_bra
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.822026569 × 1012
Minimum8.660812039 × 1011
Maximum2.546425812 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:41:04.438880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8.660812039 × 1011
5-th percentile1.027001699 × 1012
Q11.576585775 × 1012
median1.790146693 × 1012
Q32.204282911 × 1012
95-th percentile2.463108265 × 1012
Maximum2.546425812 × 1012
Range1.680344608 × 1012
Interquartile range (IQR)6.276971367 × 1011

Descriptive statistics

Standard deviation4.919653545 × 1011
Coefficient of variation (CV)0.2700099784
Kurtosis-0.5309083561
Mean1.822026569 × 1012
Median Absolute Deviation (MAD)3.641456333 × 1011
Skewness-0.263557344
Sum2.91524251 × 1013
Variance2.4202991 × 1023
MonotonicityNot monotonic
2022-07-04T13:41:04.546905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
8.660812039 × 10111
 
6.2%
1.080641864 × 10121
 
6.2%
1.368114333 × 10121
 
6.2%
1.654049713 × 10121
 
6.2%
1.6320137 × 10121
 
6.2%
2.138593266 × 10121
 
6.2%
2.546425812 × 10121
 
6.2%
2.401351848 × 10121
 
6.2%
2.435335749 × 10121
 
6.2%
2.406617082 × 10121
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
8.660812039 × 10111
6.2%
1.080641864 × 10121
6.2%
1.368114333 × 10121
6.2%
1.410301999 × 10121
6.2%
1.6320137 × 10121
6.2%
1.654049713 × 10121
6.2%
1.754150399 × 10121
6.2%
1.764276838 × 10121
6.2%
1.816016547 × 10121
6.2%
1.858109689 × 10121
6.2%
ValueCountFrequency (%)
2.546425812 × 10121
6.2%
2.435335749 × 10121
6.2%
2.406617082 × 10121
6.2%
2.401351848 × 10121
6.2%
2.138593266 × 10121
6.2%
2.02034506 × 10121
6.2%
1.858109689 × 10121
6.2%
1.816016547 × 10121
6.2%
1.764276838 × 10121
6.2%
1.754150399 × 10121
6.2%

gni_ind
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.854007214 × 1012
Minimum8.144828206 × 1011
Maximum2.804313596 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:41:04.651929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8.144828206 × 1011
5-th percentile9.033071631 × 1011
Q11.303317994 × 1012
median1.820310134 × 1012
Q32.341655037 × 1012
95-th percentile2.706574164 × 1012
Maximum2.804313596 × 1012
Range1.989830775 × 1012
Interquartile range (IQR)1.038337043 × 1012

Descriptive statistics

Standard deviation6.375718206 × 1011
Coefficient of variation (CV)0.3438885327
Kurtosis-1.094920961
Mean1.854007214 × 1012
Median Absolute Deviation (MAD)5.475512554 × 1011
Skewness-0.05812152762
Sum2.966411543 × 1013
Variance4.064978264 × 1023
MonotonicityNot monotonic
2022-07-04T13:41:04.756952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
8.144828206 × 10111
 
6.2%
9.329152772 × 10111
 
6.2%
1.211640647 × 10121
 
6.2%
1.191737412 × 10121
 
6.2%
1.333877109 × 10121
 
6.2%
1.65766032 × 10121
 
6.2%
1.80701871 × 10121
 
6.2%
1.806177662 × 10121
 
6.2%
1.833601557 × 10121
 
6.2%
2.015015377 × 10121
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
8.144828206 × 10111
6.2%
9.329152772 × 10111
6.2%
1.191737412 × 10121
6.2%
1.211640647 × 10121
6.2%
1.333877109 × 10121
6.2%
1.65766032 × 10121
6.2%
1.806177662 × 10121
6.2%
1.80701871 × 10121
6.2%
1.833601557 × 10121
6.2%
2.015015377 × 10121
6.2%
ValueCountFrequency (%)
2.804313596 × 10121
6.2%
2.673994353 × 10121
6.2%
2.631758362 × 10121
6.2%
2.622799775 × 10121
6.2%
2.247940124 × 10121
6.2%
2.079182326 × 10121
6.2%
2.015015377 × 10121
6.2%
1.833601557 × 10121
6.2%
1.80701871 × 10121
6.2%
1.806177662 × 10121
6.2%

gni_mex
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.095761627 × 1012
Minimum8.59156841 × 1011
Maximum1.283058447 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:41:04.863990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8.59156841 × 1011
5-th percentile8.784862875 × 1011
Q11.042663402 × 1012
median1.111577698 × 1012
Q31.180751136 × 1012
95-th percentile1.248789357 × 1012
Maximum1.283058447 × 1012
Range4.239016063 × 1011
Interquartile range (IQR)1.380877334 × 1011

Descriptive statistics

Standard deviation1.227564431 × 1011
Coefficient of variation (CV)0.1120284194
Kurtosis-0.3723029249
Mean1.095761627 × 1012
Median Absolute Deviation (MAD)7.147166987 × 1010
Skewness-0.4890067632
Sum1.753218603 × 1013
Variance1.506914431 × 1022
MonotonicityNot monotonic
2022-07-04T13:41:04.971015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
8.59156841 × 10111
 
6.2%
9.603977677 × 10111
 
6.2%
1.034991279 × 10121
 
6.2%
1.094332319 × 10121
 
6.2%
8.849294364 × 10111
 
6.2%
1.045220777 × 10121
 
6.2%
1.161743714 × 10121
 
6.2%
1.1778852 × 10121
 
6.2%
1.237366326 × 10121
 
6.2%
1.283058447 × 10121
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
8.59156841 × 10111
6.2%
8.849294364 × 10111
6.2%
9.603977677 × 10111
6.2%
1.034991279 × 10121
6.2%
1.045220777 × 10121
6.2%
1.049583161 × 10121
6.2%
1.05114165 × 10121
6.2%
1.094332319 × 10121
6.2%
1.128823076 × 10121
6.2%
1.141602673 × 10121
6.2%
ValueCountFrequency (%)
1.283058447 × 10121
6.2%
1.237366326 × 10121
6.2%
1.232604423 × 10121
6.2%
1.189348943 × 10121
6.2%
1.1778852 × 10121
6.2%
1.161743714 × 10121
6.2%
1.141602673 × 10121
6.2%
1.128823076 × 10121
6.2%
1.094332319 × 10121
6.2%
1.05114165 × 10121
6.2%

gni_zaf
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.566252382 × 1011
Minimum2.839269075 × 1011
Maximum4.474816508 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:41:05.079039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.839269075 × 1011
5-th percentile2.950257617 × 1011
Q13.211677209 × 1011
median3.548198005 × 1011
Q33.917843603 × 1011
95-th percentile4.295079632 × 1011
Maximum4.474816508 × 1011
Range1.635547433 × 1011
Interquartile range (IQR)7.061663939 × 1010

Descriptive statistics

Standard deviation4.831452699 × 1010
Coefficient of variation (CV)0.1354770269
Kurtosis-0.9551668043
Mean3.566252382 × 1011
Median Absolute Deviation (MAD)3.755420819 × 1010
Skewness0.2930439219
Sum5.70600381 × 1012
Variance2.334293519 × 1021
MonotonicityNot monotonic
2022-07-04T13:41:05.186063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2.839269075 × 10111
 
6.2%
2.987253798 × 10111
 
6.2%
3.233076708 × 10111
 
6.2%
3.071897333 × 10111
 
6.2%
3.231256016 × 10111
 
6.2%
4.092337597 × 10111
 
6.2%
4.474816508 × 10111
 
6.2%
4.235167341 × 10111
 
6.2%
3.911947119 × 10111
 
6.2%
3.717452347 × 10111
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
2.839269075 × 10111
6.2%
2.987253798 × 10111
6.2%
3.071897333 × 10111
6.2%
3.152940787 × 10111
6.2%
3.231256016 × 10111
6.2%
3.233076708 × 10111
6.2%
3.298187076 × 10111
6.2%
3.387666187 × 10111
6.2%
3.708729822 × 10111
6.2%
3.717452347 × 10111
6.2%
ValueCountFrequency (%)
4.474816508 × 10111
6.2%
4.235167341 × 10111
6.2%
4.092337597 × 10111
6.2%
3.935533054 × 10111
6.2%
3.911947119 × 10111
6.2%
3.782507337 × 10111
6.2%
3.717452347 × 10111
6.2%
3.708729822 × 10111
6.2%
3.387666187 × 10111
6.2%
3.298187076 × 10111
6.2%

gni_chn
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.591568122 × 1012
Minimum2.269857202 × 1012
Maximum1.458328455 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:41:05.289087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.269857202 × 1012
5-th percentile2.627706366 × 1012
Q14.975596308 × 1012
median9.002495711 × 1012
Q31.145675183 × 1013
95-th percentile1.432576732 × 1013
Maximum1.458328455 × 1013
Range1.231342734 × 1013
Interquartile range (IQR)6.481155518 × 1012

Descriptive statistics

Standard deviation4.171538145 × 1012
Coefficient of variation (CV)0.4855386218
Kurtosis-1.343641401
Mean8.591568122 × 1012
Median Absolute Deviation (MAD)3.600553787 × 1012
Skewness-0.07722100051
Sum1.3746509 × 1014
Variance1.74017305 × 1025
MonotonicityStrictly increasing
2022-07-04T13:41:05.395110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2.269857202 × 10121
 
6.2%
2.746989421 × 10121
 
6.2%
3.558383398 × 10121
 
6.2%
4.622872256 × 10121
 
6.2%
5.093170992 × 10121
 
6.2%
6.061091977 × 10121
 
6.2%
7.481123497 × 10121
 
6.2%
8.512412398 × 10121
 
6.2%
9.492579024 × 10121
 
6.2%
1.048898252 × 10131
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
2.269857202 × 10121
6.2%
2.746989421 × 10121
6.2%
3.558383398 × 10121
6.2%
4.622872256 × 10121
6.2%
5.093170992 × 10121
6.2%
6.061091977 × 10121
6.2%
7.481123497 × 10121
6.2%
8.512412398 × 10121
6.2%
9.492579024 × 10121
6.2%
1.048898252 × 10131
6.2%
ValueCountFrequency (%)
1.458328455 × 10131
6.2%
1.423992824 × 10131
6.2%
1.38337886 × 10131
6.2%
1.229427857 × 10131
6.2%
1.117757625 × 10131
6.2%
1.100877107 × 10131
6.2%
1.048898252 × 10131
6.2%
9.492579024 × 10121
6.2%
8.512412398 × 10121
6.2%
7.481123497 × 10121
6.2%

gni_wld
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.173160427 × 1013
Minimum4.773246286 × 1013
Maximum8.767011266 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.0 B
2022-07-04T13:41:05.497133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4.773246286 × 1013
5-th percentile5.086174341 × 1013
Q16.289466539 × 1013
median7.545053141 × 1013
Q38.032572587 × 1013
95-th percentile8.676940243 × 1013
Maximum8.767011266 × 1013
Range3.993764981 × 1013
Interquartile range (IQR)1.743106048 × 1013

Descriptive statistics

Standard deviation1.230132959 × 1013
Coefficient of variation (CV)0.1714910703
Kurtosis-0.6669435434
Mean7.173160427 × 1013
Median Absolute Deviation (MAD)9.296912704 × 1012
Skewness-0.6007923472
Sum1.147705668 × 1015
Variance1.513227097 × 1026
MonotonicityNot monotonic
2022-07-04T13:41:05.602157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
4.773246286 × 10131
 
6.2%
5.190483693 × 10131
 
6.2%
5.820737018 × 10131
 
6.2%
6.36899362 × 10131
 
6.2%
6.050885295 × 10131
 
6.2%
6.640083221 × 10131
 
6.2%
7.371030799 × 10131
 
6.2%
7.557225735 × 10131
 
6.2%
7.754919095 × 10131
 
6.2%
7.991305533 × 10131
 
6.2%
Other values (6)6
37.5%
ValueCountFrequency (%)
4.773246286 × 10131
6.2%
5.190483693 × 10131
6.2%
5.820737018 × 10131
6.2%
6.050885295 × 10131
6.2%
6.36899362 × 10131
6.2%
6.640083221 × 10131
6.2%
7.371030799 × 10131
6.2%
7.532880546 × 10131
6.2%
7.557225735 × 10131
6.2%
7.649008638 × 10131
6.2%
ValueCountFrequency (%)
8.767011266 × 10131
6.2%
8.646916568 × 10131
6.2%
8.499465761 × 10131
6.2%
8.15637375 × 10131
6.2%
7.991305533 × 10131
6.2%
7.754919095 × 10131
6.2%
7.649008638 × 10131
6.2%
7.557225735 × 10131
6.2%
7.532880546 × 10131
6.2%
7.371030799 × 10131
6.2%

Interactions

2022-07-04T13:40:58.796935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:20.448504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:22.333930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:24.239361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:26.148070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:28.110514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:30.141973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:32.082671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:34.010107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:36.045371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:38.194248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:40.404746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:42.555232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:44.673012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:46.624710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:48.636318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:50.678100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:52.787577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:54.830038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:56.827490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:58.885955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:20.550527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:22.426951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:24.325380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:26.240091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:28.202535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:30.230993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:32.169691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:34.101128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:36.145393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:38.295271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:40.496767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:42.652254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:44.761032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:46.720732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:48.727339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:50.770120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:52.880598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:54.923059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:56.918510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:59.051992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:20.644548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:22.520972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:24.413400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:26.337113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:28.297556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:30.325014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:32.261712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:34.258966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:36.237414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:38.398293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:40.601791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:42.754277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:44.853053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:46.821755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:48.820360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:50.866142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:52.974619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:55.019081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:57.010531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:59.143013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:20.731568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:22.611993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:24.563434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:26.431134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:28.390577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:30.417035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:32.349732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:34.348987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:36.326434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:38.496315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:40.700814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:42.850299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:44.942073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:46.921777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:48.992399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:50.967165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:53.072641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:55.115103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:57.103552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:59.238034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:20.818587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:22.703014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:24.651454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:26.527156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:28.483598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:30.511056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:32.442753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:34.444008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:36.419455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:38.686358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:40.803836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-07-04T13:40:45.986566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:48.055187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:50.034954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:52.176438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:54.188893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:56.251359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:58.226806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:00.280270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:21.879827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:23.783257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:25.628874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:27.629405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:29.597850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:31.612305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:33.543002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:35.499247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:37.675130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:39.841619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:42.041116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:44.121887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:46.152603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:48.148208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:50.129976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:52.277461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:54.280914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:56.345381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:58.322828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:00.442338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:21.973849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:23.876279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:25.722895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:27.731429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:29.693871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:31.707327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:33.639023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:35.664284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:37.781154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:39.945642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:42.145139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:44.219910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:46.251626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:48.246230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:50.225997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:52.381485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:54.374935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:56.443403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:58.421850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:00.534359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:22.059868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:23.965299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:25.808915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:27.826449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:29.791894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:31.799347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:33.730044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:35.756305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:37.880176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:40.046665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:42.245162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:44.315931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:46.344647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:48.340251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:50.385033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:52.480507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:54.464956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:56.536424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:58.512871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:00.632381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:22.150889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:24.058320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:25.965950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:27.921471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:29.887916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:31.895369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:33.824065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:35.854327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:37.984200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:40.213703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:42.350186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:44.413954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:46.438668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:48.438273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:50.480055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:52.583530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:54.563978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:56.634446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:58.608892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:41:00.727402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:22.238909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:24.148340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:26.055049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:28.015492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:29.979936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:31.988651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:33.917086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:35.949349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:38.086223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:40.309724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:42.454209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:44.509975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:46.528688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:48.539296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:50.577077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:52.685553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:54.658999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:56.730468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-04T13:40:58.702913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-07-04T13:41:05.778011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-04T13:41:05.921043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-04T13:41:06.065075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-04T13:41:06.208108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-04T13:41:00.870434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-04T13:41:01.039472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

yeardisputenon_violent_crisesviolent_criseslimited_warswarstotalgni_gergni_fragni_itagni_jpngni_cangni_rusgni_usagni_gbrgni_bragni_indgni_mexgni_zafgni_chngni_wld
01970-01-01 00:00:00.0000020056393902622742.870369e+122.234564e+121.861136e+124.938603e+121.149136e+127.454907e+111.316652e+132.578605e+128.660812e+118.144828e+118.591568e+112.839269e+112.269857e+124.773246e+13
11970-01-01 00:00:00.000002006721141043063263.045353e+122.366964e+121.957286e+124.724629e+121.298297e+129.611271e+111.406638e+132.719260e+121.080642e+129.329153e+119.603978e+112.987254e+112.746989e+125.190484e+13
21970-01-01 00:00:00.000002007791261072663443.475320e+122.713441e+122.214811e+124.720092e+121.447200e+121.270877e+121.455010e+133.091150e+121.368114e+121.211641e+121.034991e+123.233077e+113.558383e+125.820737e+13
31970-01-01 00:00:00.000002008821301023093533.780820e+122.994136e+122.386642e+125.243862e+121.530173e+121.614364e+121.471811e+132.910743e+121.654050e+121.191737e+121.094332e+123.071897e+114.622872e+126.368994e+13
41970-01-01 00:00:00.0000020091071181102583683.488175e+122.763004e+122.198544e+125.422436e+121.350956e+121.182905e+121.442627e+132.406925e+121.632014e+121.333877e+128.849294e+113.231256e+115.093171e+126.050885e+13
51970-01-01 00:00:00.000002010951081392263703.467863e+122.706419e+122.131574e+125.912477e+121.585380e+121.477813e+121.517207e+132.492138e+122.138593e+121.657660e+121.045221e+124.092338e+116.061092e+126.640083e+13
61970-01-01 00:00:00.0000020111068715519203873.845325e+122.941328e+122.289190e+126.414397e+121.759454e+121.985526e+121.584998e+132.684173e+122.546426e+121.807019e+121.161744e+124.474817e+117.481123e+127.371031e+13
71970-01-01 00:00:00.000002012998517725194053.611772e+122.742349e+122.084407e+126.445537e+121.795920e+122.140635e+121.667560e+132.691110e+122.401352e+121.806178e+121.177885e+124.235167e+118.512412e+127.557226e+13
81970-01-01 00:00:00.0000020131078217831204183.820264e+122.874860e+122.138212e+125.391606e+121.818471e+122.212869e+121.718833e+132.746457e+122.435336e+121.833602e+121.237366e+123.911947e+119.492579e+127.754919e+13
91970-01-01 00:00:00.000002014978818125194103.967012e+122.917865e+122.162198e+125.079021e+121.776572e+121.991280e+121.804309e+133.023236e+122.406617e+122.015015e+121.283058e+123.717452e+111.048898e+137.991306e+13

Last rows

yeardisputenon_violent_crisesviolent_criseslimited_warswarstotalgni_gergni_fragni_itagni_jpngni_cangni_rusgni_usagni_gbrgni_bragni_indgni_mexgni_zafgni_chngni_wld
61970-01-01 00:00:00.0000020111068715519203873.845325e+122.941328e+122.289190e+126.414397e+121.759454e+121.985526e+121.584998e+132.684173e+122.546426e+121.807019e+121.161744e+124.474817e+117.481123e+127.371031e+13
71970-01-01 00:00:00.000002012998517725194053.611772e+122.742349e+122.084407e+126.445537e+121.795920e+122.140635e+121.667560e+132.691110e+122.401352e+121.806178e+121.177885e+124.235167e+118.512412e+127.557226e+13
81970-01-01 00:00:00.0000020131078217831204183.820264e+122.874860e+122.138212e+125.391606e+121.818471e+122.212869e+121.718833e+132.746457e+122.435336e+121.833602e+121.237366e+123.911947e+119.492579e+127.754919e+13
91970-01-01 00:00:00.000002014978818125194103.967012e+122.917865e+122.162198e+125.079021e+121.776572e+121.991280e+121.804309e+133.023236e+122.406617e+122.015015e+121.283058e+123.717452e+111.048898e+137.991306e+13
101970-01-01 00:00:00.000002015908818324194043.434101e+122.491865e+121.823941e+124.619771e+121.532664e+121.325732e+121.866091e+132.886070e+121.764277e+122.079182e+121.141603e+123.387666e+111.100877e+137.532881e+13
111970-01-01 00:00:00.000002016777218821183763.555931e+122.525277e+121.882461e+125.177795e+121.509328e+121.241290e+121.902048e+132.654616e+121.754150e+122.247940e+121.049583e+123.152941e+111.117758e+137.649009e+13
121970-01-01 00:00:00.000002017687719016203713.778789e+122.653805e+121.972735e+125.113252e+121.628292e+121.532146e+121.989307e+132.664014e+122.020345e+122.622800e+121.128823e+123.708730e+111.229428e+138.156374e+13
131970-01-01 00:00:00.000002018688317325163654.105202e+122.855661e+122.115037e+125.230632e+121.695997e+121.616938e+122.094678e+132.860062e+121.858110e+122.673994e+121.189349e+123.935533e+111.383379e+138.646917e+13
141970-01-01 00:00:00.000002019719115823153584.014394e+122.787417e+122.028318e+125.323445e+121.719639e+121.639593e+122.170865e+132.862007e+121.816017e+122.804314e+121.232604e+123.782507e+111.423993e+138.767011e+13
151970-01-01 00:00:00.000002020697018019213593.953466e+122.671814e+121.915963e+125.222887e+121.627049e+121.453317e+122.128664e+132.723175e+121.410302e+122.631758e+121.051142e+123.298187e+111.458328e+138.499466e+13